Gayane Grigoryan, Tatevik Mkrtchyan, 2026. "Traceability Enhancement Between Software Requirements and Source Code Through Semantic Graph Intelligence", Journal of Machine Learning and Computational Intelligence (JMLCI) 1(1): 1-17.

In modern software engineering, Software traceability is the foundation that provides means to creating and maintaining relationships between software requirements and implementation artifacts during the whole software development lifecycle. Traditional traceability approaches that boil down to manual documentation, keyword matching, and information retrieval techniques struggle with scalability, accuracy, and maintainability as software systems grow more complex dynamically and distributed. Consequently, the traceability links are incomplete; software quality is reduced and change impact analysis as well as system evolution become cumbersome tasks. Semantic Graph Intelligence is a new paradigm, leveraging knowledge graphs, semantic representations, graph learning techniques and artificial intelligence methods to increase traceability abilities addressing its challenges. In this research, we investigate Semantic Graph Intelligence to represent software artifacts as semantic entities positioned within a graph-based ecosystem leading to improved traceability between requirements and source code. To address this issue, our proposed approach utilises knowledge in the domains of contextual understanding, semantic relationship extraction, graph neural networks as well as intelligent link prediction mechanisms to generate sensible connections between requirements and implementation components. It combines requirement semantics, source code structures, and software repository knowledge into a single shared graph to support automated traceability generation and dynamic relations discovery as well as evolution-aware maintenance. Additionally, advanced graph learning methods help to identify unseen dependencies and indirect relationships that traditional techniques struggle with. Semantic Graph Intelligence enhances the precision, recall and adaptability of traceability while enabling continuous software development practices as emphasized by the study. In addition, ExQ can improve explainability, software maintainability, auditability and impact analysis. The results prove that the semantic graph based traceability frameworks are technical, adaptive and scaleable solutions for handling complex software ecosystems with potential contribution towards building autonomous & self-evolving software engineering environments. This work lays the groundwork for future innovations in graph-driven software intelligence, neo-symbolic reasoning and AI assisted software lifecycle management to fundamentally change how traceability is achieved within next-generation software systems.

1. 1. Antoniol, G., Canfora, G., Casazza, G., De Lucia, A., & Merlo, E. (2002). Recovering traceability links between code and documentation. IEEE Transactions on Software Engineering, 28(10), 970–983.
2. 2. Borg, M., Runeson, P., & Ardö, A. (2014). Recovering from requirements traceability debt. Journal of Systems and Software, 93, 126-137.
3. 3. Cleland-Huang, J., Gotel, O., Huffman Hayes, J., Mäder, P., & Zisman, A. (2014). Software traceability: Trends and future directions. Future of Software Engineering Proceedings, 55–69.
4. 4. Cleland-Huang, J., Settimi, R., Romanova, E., Berenbach, B., & Clark, S. (2007). Best practices for automated traceability. Computer, 40(6), 27–35.
5. 5. De Lucia, A., Fasano, F., Oliveto, R., & Tortora, G. (2007). Recovering traceability links in software artifact management systems using information retrieval methods. ACM Transactions on Software Engineering and Methodology, 16(4), 1–50.
6. 6. Gotel, O., & Finkelstein, A. (1994). An analysis of the requirements traceability problem. Proceedings of the First International Conference on Requirements Engineering, 94–101.
7. 7. Hayes, J. H., Dekhtyar, A., & Sundaram, S. (2006). Advancing candidate link generation for requirements tracing. IEEE Transactions on Software Engineering, 32(1), 4–19.
8. 8. Marcus, A., & Maletic, J. I. (2003). Recovering documentation-to-source-code traceability links using latent semantic indexing. Proceedings of the 25th International Conference on Software Engineering, 125–135.
9. 9. Mäder, P., & Gotel, O. (2012). Towards automated traceability maintenance. Journal of Systems and Software, 85(10), 2205–2227.
10. 10. Spanoudakis, G., & Zisman, A. (2005). Software traceability: A roadmap. Handbook of Software Engineering and Knowledge Engineering, 395–428.
11. 11. Rahman, M. M., Roy, C. K., & Lo, D. (2018). Semantic traceability recovery from software artifacts. Empirical Software Engineering, 23(2), 1–35.
12. 12. Bavota, G., & Russo, B. (2016). A large-scale empirical study on software traceability. Information and Software Technology, 72, 145-161.
13. 13. Panichella, S., Dit, B., Oliveto, R., Di Penta, M., Poshyvanyk, D., & De Lucia, A. (2013). How to effectively use topic models for software engineering tasks. Proceedings of the International Conference on Software Engineering, 522–531.
14. 14. Hassan, A. E. (2008). The road ahead for mining software repositories. Frontiers of Software Maintenance, 48–57.
15. 15. Storey, M. A., Zagalsky, A., Filho, F., Singer, L., & German, D. M. (2017). How social and communication channels shape software development. IEEE Software, 34(1), 28–35.
16. 16. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR).
17. 17. Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 1024–1034.
18. 18. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. International Conference on Learning Representations (ICLR).
19. 19. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24.
20. 20. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W., & Leskovec, J. (2018). Graph representation learning: A review. IEEE Data Engineering Bulletin, 41(3), 52–74.
21. 21. Wang, S., Lo, D., Jiang, L., & Liu, H. (2020). Deep learning approaches for software traceability link recovery. Information and Software Technology, 125, 106–123.
22. 22. Guo, J., Cheng, J., & Cleland-Huang, J. (2017). Semantically enhanced software traceability using knowledge representation. Requirements Engineering Journal, 22(4), 435–456.
23. 23. Chen, Z., Liu, Y., & Zhao, J. (2022). Knowledge graph-driven software traceability frameworks. Journal of Systems Architecture, 126, 102–118.
24. 24. Yu, Y., Wang, H., & Li, X. (2023). Software knowledge graphs for intelligent traceability analysis. Software Quality Journal, 31(2), 411–433.
25. 25. Zhang, J., Harman, M., & Ma, L. (2021). Machine learning applications in software engineering. ACM Computing Surveys, 54(8), 1-38.
26. 26. Li, Y., Wang, J., & Zhang, H. (2021). Intelligent requirement analysis using semantic embeddings and graph learning. Information Sciences, 567, 142–160.
27. 27. Sun, Y., Han, J., Yan, X., Yu, P., & Wu, T. (2011). PathSim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment, 4(11), 992–1003.
28. 28. Alshahwan, N., Harman, M., & Minku, L. (2021). AI-assisted software engineering and intelligent software maintenance. IEEE Software, 38(5), 66–74.
29. 29. Mens, T., & Demeyer, S. (2008). Software Evolution. Springer.
30. 30. Pressman, R. S., & Maxim, B. R. (2020). Software Engineering: A Practitioner's Approach (9th ed.). McGraw-Hill Education.
31. 31. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
32. 32. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
33. 33. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT Proceedings, 4171–4186.
34. 34. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
35. 35. OpenAI. (2023). GPT-4 Technical Report. arXiv Preprint arXiv:2303.08774.